ML/AI Engineer

We are seeking a highly skilled Machine Learning / AI Engineer with hands-on experience building and deploying production-grade AI solutions. The ideal candidate will have strong expertise in Large Language Models (LLMs), PyTorch, Transformer architectures, and MLOps, with a proven track record of delivering scalable machine learning systems.

This role involves designing, training, fine-tuning, evaluating, and deploying state-of-the-art machine learning models while optimizing inference performance for real-world production environments., * Design, train, fine-tune, and deploy Machine Learning and Large Language Models (LLMs).

• Fine-tune foundation models using LoRA, PEFT, and Full Fine-Tuning techniques.
• Build scalable inference pipelines optimized for latency, throughput, and cost.
• Implement model optimization techniques such as quantization, batching, caching, and model serving optimization.
• Design high-quality datasets and data pipelines for model training and evaluation.
• Perform rigorous model benchmarking, offline/online evaluation, regression testing, and performance monitoring.
• Collaborate with Platform Engineers and DevOps teams to deploy AI models into production.
• Monitor model performance and continuously improve accuracy, efficiency, and reliability.
• Stay current with the latest AI research and apply new techniques to production systems.

• Bachelor’’’’’’'’s or Master’’’’’’'’s degree in Computer Science, Artificial Intelligence, Machine Learning, or a related field.
• 3+ years of hands-on experience in Machine Learning or AI Engineering.
• Strong programming experience with Python.
• Hands-on expertise with PyTorch and Transformer architectures.
• Experience fine-tuning LLMs using LoRA, PEFT, or Full Fine-Tuning.
• Experience deploying machine learning models into production environments.
• Strong understanding of model evaluation, benchmarking, and inference optimization.
• Experience with Hugging Face Transformers and modern LLM ecosystems.
• Knowledge of experiment tracking, model versioning, and MLOps best practices.

Preferred Qualifications

• Experience with distributed training using DeepSpeed, FSDP, or multi-GPU environments.
• Experience with Retrieval-Augmented Generation (RAG), embeddings, and vector databases.
• Knowledge of Prompt Engineering and Generative AI applications.
• Experience with Docker, Kubernetes, or cloud platforms (AWS, Azure, or Google Cloud Platform).
• Open-source contributions, research publications, or Kaggle competition experience are a plus.

Technical Skills

• Python
• PyTorch
• Hugging Face Transformers
• Large Language Models (LLMs)
• Transformer Architecture
• LoRA / PEFT
• Fine-Tuning
• Prompt Engineering
• MLOps
• Model Deployment
• Inference Optimization
• Quantization
• Model Evaluation
• DeepSpeed
• FSDP
• Multi-GPU Training
• RAG
• Embeddings
• Git
• Linux

Ideal Candidate

We’’’’’’'’re looking for someone who has gone beyond building notebooks or proof-of-concepts and has successfully delivered production AI systems. The ideal candidate should be comfortable owning the complete ML lifecycle-from data preparation and model training to deployment, monitoring, optimization, and continuous improvement.

If you’’’’’’'’re passionate about building next-generation AI applications and have hands-on experience with modern LLM technologies, we’’’’’’'’d love to hear from you.